Two metaheuristics for solving no-wait operating room surgery scheduling problem under various resource constraints

•This paper considers the research area of surgical scheduling problem with various stages and resources.•Two meta-heuristics are provided: iterated local search and hybrid genetic algorithm.•The operating rooms are reduced and the surgeries assignments to different operating rooms and nurses are ba...

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Bibliographic Details
Published in:Computers & industrial engineering 2018-12, Vol.126, p.494-506
Main Authors: Belkhamsa, Manel, Jarboui, Bassem, Masmoudi, Malek
Format: Article
Language:English
Subjects:
Hybrid genetic algorithm
Iterated local search
Multi-resource constraints
Multi-stages
Surgery scheduling
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Summary:•This paper considers the research area of surgical scheduling problem with various stages and resources.•Two meta-heuristics are provided: iterated local search and hybrid genetic algorithm.•The operating rooms are reduced and the surgeries assignments to different operating rooms and nurses are balanced.•A multi-objective function is considered and the reduction of opening operating rooms is studied.•Computational experiments show that our meta-heuristics outperform the current state-of-the- art algorithm. The problem studied in this paper is operating room surgery scheduling, with resource constraints in each of the three following stages: preoperative, intraoperative, and postoperative stages. The availability of material resources, specialties and qualifications of human resources are integrated, and the aim is to schedule surgeries while minimizing the maximum end time of last activity in stage 3 and the total idle time in the operating rooms. Two metaheuristics, an iterative local search approach and a hybrid genetic algorithm, are provided and tested on real workday instances from the literature. Computational experiments showed that our metaheuristics outperformed the current state-of-the-art solving algorithm which is an ant colony optimization. The hybrid genetic algorithm reached small superiority vs. the iterative local search algorithm. The average reduction in the end time (the total idle time) was 24% (59%) with the iterated local search approach and 24% (70%) with the hybrid genetic algorithm vs. 14% (55%) with the ant colony optimization algorithm.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2018.10.017